Derivation and Calculation of Approximate Reliabilities and Daughter Yield-Deviations of a Random Regression Test-Day Model for Genetic Evaluation of Dairy Cattle

Binational genetic evaluation between Germany and France were performed for each type trait using a single-trait MACE (multiple across-country evaluation) model. Daughter yield deviations (DYD) of bulls having 30 equivalent daughter contributions or more were the data for parameter estimation. Full pedigree information of bulls was used via sire and dam relationships. In general, across-country genetic correlation estimates were in agreement with what is observed by Interbull. The estimated correlations were over 0.93 for stature, rump angle, udder depth, front teat placement, teat length and rear teat placement. These traits have been classified in both countries for a long period of time. However, some other type traits were included later in the French type classification system (most of them since 2000): chest width, body depth, angularity, rump width, rear leg rear view, fore udder and rear udder height. The estimated correlations for these traits were relatively low. In order to check changes in genetic correlations over time, data from bulls born until the end of 1995 were discarded. Higher genetic correlation estimates between both countries were obtained by using more recent data especially for traits having lower genetic correlation, e.g. body depth correlation increased from 0.55 to 0.83. Once genetic correlations were estimated, binational genetic evaluation between Germany and France were performed for each type trait using DYD of bulls. The rankings of bulls obtained from this evaluation had some differences with Interbull rankings but a similar proportion of bulls from each country was found. Finally, more computationally demanding binational evaluations were performed using yield deviations of cows for binational cow comparison. The rankings obtained were influenced by the number of daughters per bull and heritabilities used in each country.

show abstract

“…The multiple-trait EDC procedure (Liu et al, 2004a) can be used to approximate matrix W for each animal.…”

Section: Datamentioning

confidence: 99%

Binational evaluation of type traits from Germany and France with a single-trait MACE animal model

Pujol

Liu²,

Reinhardt³

et al. 2009

Animal

Self Cite

View full text Add to dashboard Cite

show abstract

“…The EDC conversion approach of Sullivan and Wilton (2001) attempts to create new EDC values, by which the estimated genetic correlations will work as if there were non-zero residual correlations. An alternative to the EDC approach of Sullivan and Wilton (2001) is the EDC approach of Liu et al (2004). Calculation of block multi-trait EDC matrices (Liu et al, 2004) requires information that is only available at the national level.…”

Section: Genetic Correlationsmentioning

confidence: 99%

“…An alternative to the EDC approach of Sullivan and Wilton (2001) is the EDC approach of Liu et al (2004). Calculation of block multi-trait EDC matrices (Liu et al, 2004) requires information that is only available at the national level. However, the approach of Sullivan and Wilton (2001) can be implemented at the international level without detailed knowledge about distribution of daughters across herds and traits.…”

Section: Genetic Correlationsmentioning

confidence: 99%

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

Nilforooshan

Jakobsen

Fikse

et al. 2014

Animal

View full text Add to dashboard Cite

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, acrosscountry selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.

show abstract

“…, [6] and where matrix H = matrix calculated with variance-covariance matrix G and additive genetic relationships matrix A. EDC in the matrix R determines in the model the weight of the sire based on the number of daughters for the estimated BV (Liu et al, 2004). In this case, EDC is unknown if it is evaluated from the reliability coefficient of NBV and heritability coefficient of the trait (formulas 7 and 8).…”

Section: Methods Bmentioning

confidence: 99%

“…In a first step, the BVs were de-regressed and a vector with the daughters' yield deviations (DYD) values was obtained (Liu et al, 2004). Subsequently, the multitraits model was calculated for each individual sire (best linear unbiased prediction method).…”

Section: Methods Bmentioning

confidence: 99%

Methods for calculating the breeding values of missing traits and their comparison

Krpálková¹,

Přibyl²,

Vostrý³

et al. 2014

JCEA

View full text Add to dashboard Cite

The objective of this study was to calculate the breeding values (BVs) of traits missing in a selection index. Different traits can be evaluated within the breeding programs of given countries. The BV of a trait can be calculated based on genetic correlations with other traits. Similarly, the BV of a missing trait can be calculated for imported bulls. Two methods of calculation were used. Method A was based on a regression of BVs. Method B was based on performing a de-regression of BVs and their retroactive calculation. Both of these methods were tested using a Czech and a Canadian database of BVs for Holstein bulls. The Czech database of Holstein bulls contained 766 bulls and the Canadian database 851. Two calculations were performed for bulls with low reliability of estimated BVs, the first calculation with their genetic correlation matrix and the second with a genetic correlation matrix created from a set of bulls with high reliability of BVs. These newly calculated BVs (CBVs) were then compared with the national BVs (NBVs) using correlation coefficients. The highest correlations were achieved with high reliability bulls when all traits were included into the calculation (34 evaluated traits). The correlations of these bulls averaged 0.82, with an average standard deviation of 0.19. The lowest correlations were found when low reliability bulls were included and the genetic correlation matrix from the high reliability bulls was applied. That average correlation was 0.74 and standard deviation 0.25. When only 15 traits were evaluated in the model, the average correlation for all sets was 0.68 with standard deviation of 0.28. These results show that calculating the BV of a missing trait is possible using both methods. Method B was slightly more accurate in its prediction. AbstraktCílem této studie bylo dopočítat plemenné hodnoty (PH) býků, které chybí v selekčním indexu. V rámci šlechtitelských programů v jednotlivých zemích mohou být hodnoceny různé znaky. PH znaků lze vypočítat na základě genetických korelací s jinými znaky. Podobně lze PH chybějícího znaku vypočítat i pro importované býky. Byly použity dvě metody výpočtu. Metoda A byla založena na regresi PH. Metoda B byla založena na odregresování PH a na následném zpětném výpočtu chybějící PH pomocí korelací. Obě metody byly testovány na české a kanadské databázi PH holštýnských býků. Česká databáze holštýnských býků obsahovala 766 býků a kanadská databáze 851. U býků s nízkou spolehlivostí odhadovaných PH byly provedeny dva výpočty, první výpočet na základě jejich vlastní genetické korelační matice a druhý pomocí genetické korelační matice vytvořené podle souboru býků s vysokou spolehlivostí PH. Tyto nově vypočtené PH (CBVs) pak byly porovnávány s původními národními PH (NBVs) pomocí korelačních koeficientů. Nejvyšší korelace byly dosaženy u býků s vysokou spolehlivostí odhadu, kde byly zahrnuty všechny znaky ve výpočtu (34 znaků). Korelace u těchto býků činili v průměru 0,82 a průměrná směrodatná odchylka byla 0,19. Nejnižší korelace byly zjištěn...

show abstract

Derivation and Calculation of Approximate Reliabilities and Daughter Yield-Deviations of a Random Regression Test-Day Model for Genetic Evaluation of Dairy Cattle

Cited by 53 publications

References 15 publications

Binational evaluation of type traits from Germany and France with a single-trait MACE animal model

Binational evaluation of type traits from Germany and France with a single-trait MACE animal model

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

Methods for calculating the breeding values of missing traits and their comparison

Contact Info

Product

Resources

About